Training a Classifier — PyTorch Tutorials 2.7.0+cu126 documentation
Understanding PyTorch’s Tensor library and neural networks at a high level. Train a small neural network to classify images
Transfer Learning for Computer Vision Tutorial - PyTorch
In this tutorial, you will learn how to train a convolutional neural network for image classification using transfer learning. You can read more about the transfer learning at cs231n notes Quoting these notes,
Welcome to PyTorch Tutorials — PyTorch Tutorials 2.7.0+cu126 …
Train a convolutional neural network for image classification using transfer learning. Image/Video
PyTorch Examples — PyTorchExamples 1.11 documentation
This pages lists various PyTorch examples that you can use to learn and experiment with PyTorch. Image Classification Using ConvNets This example demonstrates how to run image classification with Convolutional Neural Networks ConvNets on the MNIST database.
TorchVision Object Detection Finetuning Tutorial — PyTorch …
In this tutorial, you have learned how to create your own training pipeline for object detection models on a custom dataset. For that, you wrote a torch.utils.data.Dataset class that returns the images and the ground truth boxes and segmentation masks. You also leveraged a Mask R-CNN model pre-trained on COCO train2017 in order to perform ...
Optimizing Vision Transformer Model for Deployment - PyTorch
Facebook Data-efficient Image Transformers DeiT is a Vision Transformer model trained on ImageNet for image classification. In this tutorial, we will first cover what DeiT is and how to use it, then go through the complete steps of scripting, quantizing, optimizing, and using the model in iOS and Android apps.
Whole Slide Image Classification Using PyTorch and TIAToolbox
In this tutorial, we will show how to classify Whole Slide Images (WSIs) using PyTorch deep learning models with help from TIAToolbox. A WSI is an image of a sample of human tissue taken through a surgery or biopsy and scanned using specialized scanners.
ResNet - PyTorch
All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225] .
Training with PyTorch — PyTorch Tutorials 2.7.0+cu126 …
The Tutorials section of pytorch.org contains tutorials on a broad variety of training tasks, including classification in different domains, generative adversarial networks, reinforcement learning, and more
DCGAN Tutorial — PyTorch Tutorials 2.7.0+cu126 documentation
As mentioned, the discriminator, \(D\), is a binary classification network that takes an image as input and outputs a scalar probability that the input image is real (as opposed to fake). Here, \(D\) takes a 3x64x64 input image, processes it through a series of Conv2d, BatchNorm2d, and LeakyReLU layers, and outputs the final probability through ...